Using Male Earnings Data to Forecast the Future Income of Females

This article first appeared in the autumn 1997 issue of the Expert Witness.

In two recent British Columbia judgments ([B.I.Z.] v. Sams, [1997] B.C.J. No. 793; and Terracciano v. Etheridge and Fujii, [1997] B.C.S.C. B943125), the court accepted use of average earnings statistics for males to estimate the future earnings of female plaintiffs. In this article, I investigate the reasoning behind the court’s decision, and the circumstances in which it might be appropriate for us to use male data when estimating female earnings.

As an introduction, I first consider some statistical evidence regarding the “wage gap” between men and women. How much of this gap is due to discrimination, and how much can be explained by other factors? Second, I examine how the wage gap has diminished somewhat over time. Finally, I consider the implication of the economic evidence, together with the recent court decisions.

The Wage Gap

As we know, women, on average, tend to earn less money than men. In fact, in 1991, average income for women was only 61.5 percent of that for men. However, part of this gap is because a higher proportion of women workers are part-time. If we compare women working full-time to men working full-time, we find that (in 1991), women earned about 70 percent as much as men.

However, a wage gap of about 30 percentage points remains. Can this gap be explained by educational differences – are women earning less than men simply because they do not invest in as much education? The answer is no. Even when researchers compare men and women with the same level of education, the wage gap remains. For example, statistics indicate that Canadian women with university degrees earn only 72 percent as much as Canadian men with degrees. Some, but not all, of this wage gap is due to discrimination in the labour market.

In fact, it appears that much of the wage gap is due to factors other than labour market discrimination. One source is that women tend to work fewer years in total, over their careers, than do men – they have a greater tendency to interrupt their careers and withdraw from the labour force (especially to raise children), and they are also more likely than men to work part-time. These factors are reflected in labour force statistics which indicate that while 95 percent of 25-44 year old male workers are employed full-time, only 77 percent of women workers of the same age are full-time. These factors effectively reduce the average amount of work experience that women accumulate over their careers (part of what economists call human capital). So because women, on average, bring less experience to their jobs, they also tend to earn lower incomes at any given age.

Based on this, we might expect that if we compared men and women in the same jobs, with the same education, and the same amount of work experience, the wage gap would disappear. However, that is not the case. A Canadian study examined this question by surveying men and women who graduated from Canadian universities in 1982 and comparing their annual incomes two years and five years after graduation. The study compared men and women who had completed the same type of degree in university and who had worked continuously over the study period. The conclusion was that, even when controlling for education and experience factors, women still earned less than men – after two years the study found that the women graduates were earning 88 percent as much as men, and after five years they were earning about 82 percent. This trend was visible even for women with master’s and doctoral degrees (though women with doctorates in medical and health sciences were earning more than their male counterparts after two and five years). A particularly notable result is that, on average, the gap between men’s and women’s earnings actually increased as their careers progressed.

This leaves an unexplained wage gap of at least 10 percent – it is this portion of the gap which is generally attributed to discrimination. However, not all of this “discrimination wage gap” is due to discrimination in the labour market. Some of it seems to be due to the type of career paths that women tend to choose within occupations – perhaps they are positioning themselves somewhat for a future point at which they expect to temporarily withdraw from the labour force or drop to part-time status. It also seems that, to some extent, women tend to be socialized – within their families, in school, and culturally – to choose different sorts of career paths than men. The portion of the wage gap that remains after accounting for these factors is due to labour market discrimination – maybe 3-5 percent.

Note however, that the tendency for women to be socialized toward lower-paying careers may result from systemic discrimination – discrimination between boys and girls in the way that they are raised. It is also sometimes argued that traditional women’s occupations are lower-paid because women predominate in these professions (to the extent that this is true, the labour market is responsible). If we include these forms of discrimination, then the total wage gap due to discrimination is more like 10-15 percent.

The Wage Gap: Changes Over Time

When deciding whether to rely on historical income statistics to forecast future earnings, it is important to consider whether the historical relationships of the past can be expected to apply in the future. Examination of historical average earnings statistics for men and women working full-time reveals that the average wage gap has shown a clear decreasing trend over time. Specifically, the ratio of average female earnings to average male earnings increased from 59.7 percent in 1971 to 73.1 percent in 1995. This trend is illustrated in Figure 1 below.

Figure 1: Ratio of Average Earnings of Females to Average Earnings of Males

If we adjust average earnings statistics for the effects of inflation (so that, for example, earnings in 1971, 1981, and 1991 are all expressed in 1996 dollars), we discover that average male earnings only benefited from slight real increases over the last 25 years. (Specifically, average male earnings grew at approximately 0.10 percent per year over 1971-95.) The average earnings of women, on the other hand, experienced noticeable real growth – approximately 1.28 percent annually over 1971-95. This suggests that the male-female wage gap is decreasing over time because women are experiencing significant real wage gains, while men are not. We suspect that this trend is largely due to women spending more time in the labour force (increasing participation rates), pursuing higher paying occupations (including many “traditional male occupations”), and facing less discrimination than in the past. The annual real wage gains of men and women over this period are shown in Figure 2 below.

Figure 2: Real Changes in Earnings of Males and Females

We noted above that women tend to participate less in the labour market than men – they interrupt their careers more often, and for longer periods of time. The tendency toward work interruptions among women is changing though – recent information from Statistics Canada indicates that women’s labour force interruptions are now significantly shorter than they were in the past: over half of all Canadian women now return to work within two years of an interruption, compared with only an eighth in the 1950s. As it continues, this trend will further narrow the wage gap. We also know that women with more education tend to return earlier to the labour force.

Given these trends, past earnings averages for women will not accurately reflect what the average woman will earn in the future. Women are catching up to men, and it seems reasonable that today’s young women can expect to earn approximately the same lifetime income as today’s young men, if they follow similar career paths.

Implications

What do these findings tell us about when we should use earnings statistics for men to forecast the future earnings of a woman? It seems that this would be appropriate if we have reason to believe that the woman involved would have followed a career path more typical of men than of women (historically). For example, if it is believed that a young woman would have worked full-time (or very nearly so), and without interruption, throughout her career, then it would appear to be appropriate to use earnings data for males in her occupation.

Of the two BC judgments noted at the beginning of this article, one involved a woman who had already established her career path at the time of the accident, while the other involved a young girl who had not completed high school. In the former, it was apparently reasonably clear that she was following the sort of career path that has been typical of males, rather than females. In the latter, it was argued that the girl would have followed a typical male career path. In either of these types of situations it seems that using average male earnings statistics will better predict what a woman’s future earnings will be (or would have been, but for an injury or death).

However, what if a plaintiff has not established a career path at the time of her injury, and it is unclear whether she would have followed a typical male or a typical female career path?

In these cases, economists have typically chosen to forecast a young woman’s income based on her expected level of education – using statistics representing average earnings for women with a certain level of education. Our discussion so far may seem to suggest that average earnings for males of the given education level might be a better choice than using that for females. However, there are some difficulties with this approach. As noted, on average, women tend to enter different careers than men, even when they are working full-time (that is, we still observe a trend of “typical male occupations” and “typical female occupations”). And the typical female occupations tend to pay less. Given this, we would expect that the average income for women of a given education will continue to be less than the average for men of the same education – even if the women are working full-time without interruption. This holds even if we believe that labour market discrimination will end.

This suggests that using male earnings data to forecast the earnings of a young woman might overstate the woman’s true earning potential if we are basing our income estimates solely on a given education level, rather than on a given education and a given occupation. (Though, using earnings data for females will almost certainly underestimate the earnings potential of a young woman.)

Alternatively, if one is calculating the young woman’s potential income by assuming that she would have worked at a specific occupation (as an economist, for example), then it would probably be more accurate to rely on male earnings data, and then explicitly apply contingencies reflecting the impact of possible labour force absences and part-time employment. By using historical data for males, we can hopefully correct for the errors introduced when we use historical data for women (which reflects women who followed different career paths and faced greater discrimination than women today and in the future). By directly applying the appropriate contingencies for non-participation and part-time employment, based on our knowledge of the particular plaintiff, we will adjust for the probability that the woman may or may not have followed a “traditional” woman’s career path. These two adjustments will allow us to determine a reasonable forecast of a woman’s earnings, knowing that even if she follows a “traditional” career path, she will likely not face the same degree of discrimination as faced by past women whose earnings formed the basis for current statistical averages.

We should emphasize, however, that these generalizations can always be overridden by the facts of a particular case. If it is reasonable to assume that a young girl would have followed a career path more typical of men than of women (even if we do not know what that career would have been), then it is also reasonable to use male earnings data to forecast her income.

Derek Aldridge is a consultant with Economica and has a Master of Arts degree (in economics) from the University of Victoria.